ABSTRACT

With the booming exploration and development of unconventional hydrocarbon resources in source rocks, the estimation of total organic carbon (TOC) content from well logs has become increasingly important because of the significance of TOC in the formation evaluation of those resources. In this paper, a new log overlay method is developed to estimate the TOC content of source rocks with excess radioactivity, but containing little or no potassium feldspar. Specifically, on the basis of previous results of log responses of source rocks, it is believed that the natural gamma ray (GR) log responses of source rocks in the applicable conditions are predominantly contributed by clay minerals and organic matter. A practical clay indicator is established to reflect the clay content using density and neutron logs. The indicator is effective not only in nonsource rocks that contain oil or water but also in source rocks. Furthermore, a new method was developed by overlaying the properly scaled clay indicator curve on the GR curve. In nonsource rocks, including clay-rich rocks and reservoirs saturated with oil or water, the two curves overlie each other, whereas a separation between the curves occurs in organic-rich source rocks. The separation between the curves was defined and expressed and can be used to calculate the TOC consecutively after careful calibration with core data. This method has been successfully applied to two shale gas plays with high-maturity kerogen in the Sichuan Basin, China. In addition, a source rock with low-maturity kerogen was used to verify the new method for its effectiveness, reliability, and widespread adaptability.

INTRODUCTION

Estimating the organic matter richness in source rocks is essential for studying the potential for generating oil and gas resources and is a necessary part of the exploration for oil and gas (Zhang et al., 1999). Total organic carbon (TOC) content is an indicator of organic matter richness (Zhang et al., 1999; Wang and Guo, 2000). It can be determined directly by geochemical analyses of source rock samples, including sidewall cores and formation cuttings. However, it is not practical to obtain samples from all intervals of all wells in any source rock play. The estimation of TOC from well logs has become increasingly important with the booming exploration and development of unconventional shale oil, shale gas, and tight oil resources worldwide, because TOC is one of the most significant parameters in formation evaluation for those resources, in addition to well-logging techniques that can help offset the discontinuity of core sample analysis.

Many methods for estimating TOC content from well logs have been published by geoscientists and log analysts. Supernaw et al. (1978) proposed a method for the evaluation of TOC content in shale strata based on natural gamma ray spectral logs. They carried out the empirical correlations of TOC with the uranium/potassium (U/K) ratio and with U content in the Devonian New Albany Shale, Illinois Basin, United States. Bulk density (DEN) logs were used to evaluate the TOC of shaly source rocks by Schmoker (1979). The gamma ray (GR) logs were used to estimate TOC in the organic-rich shale of the Appalachian Basin by Schmoker (1981) through the correlation of TOC with GR logs. Dellenbach et al. (1983) introduced a parameter Ix that combined acoustic (AC) and resistivity (RT) logs to correlate with organic matter richness. Furthermore, Fertl and Chilingar (1988) used a multivariate statistical method to construct an evaluation model that takes the GR, neutron (CNL), DEN, and AC logs as input parameters. A ΔlogR method that overlays the AC and RT logs was proposed by Passey et al. (1990). Recently, a method based on the difference between the grain density and inorganic grain density was introduced by Jacobi et al. (2008). The grain density is computed by the nuclear magnetic resonance (NMR) log porosity and DEN log, and the inorganic density is calculated from mineralogy from geochemical logs. The difference between the two grain densities can be used to access the TOC after calibration with core data.

Each method, even the ΔlogR model, requires sample-to-log calibrations for validation. Regardless, the above methods can be divided into two categories, one of which is direct calibration using core data, and the other overlays two log curves and then attempts to transform the separation of the two logs to TOC. The ΔlogR model can distinguish source rocks and nonsource, clay-rich rocks. The AC and RT overlie in nonsource, clay-rich rocks but are separate in source rocks. Additionally, the two logs separate in nonsource reservoirs. The Jacobi et al. (2008) method can differentiate source rocks and nonsource rocks reasonably well. That is because the grain densities from DEN and NMR porosity logs and from geochemical logs are approximately the same in nonsource rocks. The two grain densities would separate because the grain density from DEN and NMR porosity logs is the average of the organic matter and inorganic minerals. In contrast, the direct core calibration methods cannot provide this information. Therefore, the second log overlay category is better than the first category when evaluating TOC content from logs.

Among the second category of methods, the Jacobi et al. (2008) method is advanced and accurate. However, it cannot be widely used because NMR and geochemical logs are not always available because of the high cost of NMR and geochemical logging techniques relative to other common logging tools. The ΔlogR model is the most widely used. It has established a relationship of ΔlogR (i.e., a separation between AC and RT) with maturation, which has been successfully applied in many oil and gas fields (Wang et al., 2002; Kamali and Mirshady, 2004; Huo et al., 2011; Alfred and Vernik, 2012). However, the ΔlogR technique failed to evaluate TOC in our study gas shales, which have abnormal RT responses that change rapidly within the interval of interest. Even worse, in the study basin, there are source-rock intervals with vitrinite reflectances (Ro) greater than 2.5% in several wells where the RT is lower than that of the nonsource, clay-rich rocks (Wang et al., 2009b; Yang et al., 2012), which is totally opposite the theory of the ΔlogR method.

Based on previous results by geoscientists and log analysts for log responses of source rocks and different log responses for common minerals of sedimentary rocks, this paper develops a new method that combines DEN, CNL, and GR logs to estimate the TOC of source rocks. The new method is then demonstrated and verified for its effectiveness and reliability by implementing it on the shale gas reservoirs in the Sichuan Basin, China, and an example from Passey et al. (1990).

GEOLOGICAL SETTING

The Sichuan Basin, which is located in Southwest China, is surrounded by thrust belts on all sides (Liu et al., 2011; Xu et al., 2015) (Figure 1A, B). Black shale, dark-gray mudstones, and shale gas are extensively developed in the Silurian Longmaxi Formation and Cambrian Qiongzhusi Formation of the Sichuan Basin and surrounding areas. According to the China Geological Survey (Ye et al., 2014), the geological reserves and economically recoverable reserves of shale gas in the basin are estimated to be 4.02 × 1013 and 6.44 × 1012 m3, respectively. Additionally, the Fuling area in the eastern Sichuan Basin has been developed into a large shale gas field. The total production of the Longmaxi and Wufeng Formations from 28 wells ranges from 1.16 × 105 to 5.47 × 105 m3 per day (Guo, 2014). Two shale gas fields, named YuDongNan (YDN) and ChuanXiNan (CXN), were chosen as the study areas in this paper. The YDN area is located in the Hunan–Hubei–Guizhou fold thrust belt, which is close to the eastern edge of the Sichuan Basin, and the CXN area is located in the southwest Sichuan Basin (Figure 1B). The shale gas exploration in the YDN area occurs in the Longmaxi Formation and the Ordovician Wufeng Formation. The Ro of the target shale range from 2.5% to 3.5%, and the kerogens are types I and II (Wang et al., 2009b; Jia, 2015). The target formation in CXN is the Longmaxi Formation. The Ro range from 2.2% to 3.6%, and the kerogen is type I (Zhou, 2013). The stratigraphic columns of the two fields are illustrated in Figure 2. In addition, the target formations of the two fields were deposited in a marine sedimentary environment.

SAMPLES AND MEASUREMENTS

For this study, 68 core samples were collected from well SS in the central YDN area, and 15 core samples were collected from well TT in the northern CXN area. The samples from YDN were taken from depths of 727–803 m (2385–2635 ft), and the samples from the CXN area were taken from depths of 1495–1550 m (4905–5085 ft). To verify the clay indicator proposed in this paper, 35 core samples from the YDN field were used for an x-ray diffraction (XRD) analysis. The rest of the samples were used for the TOC content analysis.

The XRD analysis was performed on the nonoriented powdered samples using an x-ray diffractometer to determine the mineralogy of the samples, following the Chinese oil and gas industry standard SY/T 6201-1996. The diffractometer was equipped with a copper x-ray tube that operated at 40 kV and 40 mA. The scan angle range was 5°–90°. The TOC content was determined by combustion. The carbonates were removed from the rock sample of interest with hydrochloric acid before combustion, because those minerals would yield carbon dioxide during the combustion. The TOC analyses were then run in the LECO carbon analyzer that combusts a 140-mg sample of powdered rock at 1300°F (704.4°C) in the presence of a large excess of oxygen. The experimental data are listed in Tables 1 and 2.

THEORY AND METHODOLOGY

Basic Theory

The source rocks are generally shales and lime mudstones that contain significant amounts (>2%) of organic matter (Zhang et al., 1999; Wang and Guo, 2000). The nonsource rocks may also contain organic matter, but the amount is commonly so small that they can be neglected. For the purpose of this paper, the petrophysical models shown in Figure 3A, B were assumed for the nonsource and source rocks, respectively. The model for the nonsource rock includes clay minerals, nonclay minerals (possibly sandstone minerals and/or carbonate minerals), and pore fluids and is shown in Figure 3A. However, Figure 3B demonstrates that the model for the source rock contains an additional component, kerogen, that is not present in the nonsource rocks. Kerogen is an insoluble material formed by the degradation of organic matter and is a required ingredient for the generation of hydrocarbons.

To evaluate TOC using well logs, it is necessary to be familiar with the physical or chemical properties of organic matter. Fortunately, studies of petrophysicists and geochemists on the properties of kerogen offer us the required information. Compared with other common minerals in sedimentary rocks, kerogen has a low density, low velocity, no or low conductivity, high radioactivity, and high hydrogen content (Fertl and Chilingar, 1988; Zhang et al., 1999; Lewis et al., 2004; Ward, 2010). It has also been found that the kerogen in some lacustrine source rocks has no excessive GR activity because of the scarcity or absence of U ions in freshwater environments (Fertl and Chilingar, 1988; Lin and Salisch, 1993). This paper focuses on kerogens with high GR activity, because kerogens from marine and some continental source rocks have that property.

The unique characteristics of kerogen make it viable and available for identifying source rocks and estimating TOC content using well logs. Table 3 summarizes common log responses in source rocks. It can be observed that each of the common logs in source rocks is influenced by kerogen. It is known that GR logs measure the radioactivity of rocks (Schlumberger, 1989). Porosity logs (DEN, CNL, and AC) reflect a weighted average result of inorganic minerals, kerogen, and porosity. The RT is a complicated function of porosity, water saturation, clay content and its distribution, pore structures and wettability, and other rock characteristics. Figure 3B illustrates log responses contributed by rock components. From this figure, each common log responds to each component of the source rocks, except for GR logs, which do not respond to fluids. Consequently, GR logs are prioritized to evaluate TOC content because of the fewer potential component contributions to the log variation.

Table 4 presents the GR values for common minerals in sedimentary rocks, among which illite, montmorillonite (also called smectite), chlorite, and kaolinite are clay minerals; quartz, plagioclase, and K-feldspar are common minerals in sandstone; and calcite and dolomite are carbonates. The GR response values of clay minerals are higher than those of nonclay minerals, except for K-feldspar. This is the reason why GR logs are used to calculate shale or clay volumes in formation evaluations for nonsource rocks without K-feldspar. The method developed below is based on the assumption that source rocks contain little or no K-feldspar because of the high GR values for K-feldspar.

The GR response values are usually high in source rocks, which is caused by the high U content in organic matter. For this reason, it is suggested that the GR responses of source rocks are predominantly contributed by clay minerals and kerogen rather than nonclay minerals with low GR values. If a parameter can be developed to indicate the clay mineral content and used to strip the contribution of clay minerals from GR log responses in source rocks, then TOC could be estimated from the residual responses.

Practical Clay Indicator

The DEN and CNL log responses for sedimentary rocks are physically dependent and correlated (Raymer and Biggs, 1963; Burke et al., 1969; Poupon et al., 1970). The physical dependencies of DEN and CNL logs were also examined through a complex derivation of the two log principles by Mao (2001). A crossplot of DEN and CNL can be used to identify and determine lithology. It was also found that the apparent neutron porosity of shale is greater than the apparent density porosity. Herein, the apparent porosity means that calcite was assumed as the matrix because the CNL log response is typically calibrated in limestone. The apparent neutron porosity and density porosity are given by equations 1 and 2, respectively:(1)(2)where ϕNa is the apparent neutron porosity of the limestone calibration in volume/volume; ΦN is the CNL log value in porosity units; ϕDa is the apparent density porosity of the limestone calibration in volume/volume; ρb is the DEN log value in grams per cubic centimeter; ρma is the density value of limestone, 2.71 g/cm3; and ρf is the fluid density value, 1.0 g/cm3.

The DEN and CNL log values of common minerals in sedimentary rocks were summarized, and the differences between the apparent neutron porosities and density porosities of those minerals were then calculated and are listed in Table 5. It can be observed that the differences between the apparent neutron and density porosities for clays were obviously larger than those for nonclay minerals. In view of this, the difference between the apparent neutron and density porosities is defined as the clay indicator (Icl), which functions similarly to GR logs in nonsource rocks and should also work in source rocks. The clay indicator is described as(3)

Density, Neutron Log Value, and Differences between the Apparent Neutron and Density Porosities of Minerals in Sedimentary Rocks

The difference between the apparent CNL and DEN porosities for kerogen should be similar to that of the nonclay minerals if the clay indicator works in source rocks. The kerogen density published by Waters et al. (2007) ranges from 1.15 to 1.65 g/cm3. Ward (2010) proposed that the density of kerogen in the Devonian Marcellus Shale of the Appalachian Basin, United States, equals Ro × 0.342 + 0.972. Mastalerz et al. (2012) reported that in immature kerogen, the alginite density is less than 1.15 g/cm3, the amorphinite density is between 1.2 and 1.4 g/cm3, and densities between 1.15 and 1.20 g/cm3 correspond to mixtures of alginite and amorphinite. The DEN of low-maturity kerogen is 1.15 g/cm3 in this paper. The CNL log responses of kerogen, especially the variations of log responses for maturing kerogen, have not been discussed in the literature to date. Zhao et al. (2015) investigated the responses of DEN and CNL logs in coal beds with different ranks and concluded that the CNL log shows a complicated response trend as the coal rank varies. The neutron porosity of kerogen of low maturity is determined by the Zhao et al. (2015) method. The neutron porosities for oil-prone type I and II kerogens of low maturity were estimated to be approximately 86 porosity unit. Thus, the difference between the apparent CNL and DEN porosities was approximately −5.22%, which was lower than the value of the clays and was close to the value of the quartz, which indicates that the clay indicator may be used to indicate the clay volume in source rocks with low-maturity kerogen. Source rocks with high-maturity kerogen will be illustrated in the Case Studies section.

Note that the clay indicator will be not effective in nonsource rocks that contain significant amounts of gas because of the excavation effect of CNL logging. However, the case is different in shale gas reservoirs. Although Zhao (2013) pointed out that high free gas content might cause the excavation effect of CNL logging, the excavation effect in shale gas reservoirs has not been verified or found on field logs. Thus, the effect of free gas on CNL logs in gas shale was neglected.

New Overlay Method

The GR logs respond primarily to clays and kerogen. In nonsource rocks, the variations in GR logs and the clay indicator are the same or are similar. Conversely, in source rocks, the two curves will present different variations (Figure 4). In applications, the GR log and clay indicator are displayed in the same track. The scales of the two curves should be properly adjusted to make them overlie for nonsource rocks, in terms of the clay-rich rocks and reservoirs with oil or water. When the two curves are scaled, organic-rich intervals can be recognized by the separation of the two curves. The determination of the track scales of the curves is presented in the following section. The separation between the two curves, which is designated as Δd, is expressed as(4)where(5)and(6)where GR is the log value in API gravity, GR_left is the left scale of the GR curve in API gravity, GR_right is the right scale of the GR curve in API gravity, Icl_left is the left scale of the clay indicator curve, and Icl_right is the right scale of the clay indicator curve.

A schematic of the new overlay method. In source rocks, the two curves overlie each other. In nonsource rocks, the curves separate. GR = gamma ray log; Icl = clay indicator curve.

The Δd separation increases as the kerogen increases. If a relationship between the core TOC and Δd is established, then the TOC of the wellbore section, where no samples are available, can be calculated. In this paper, the relationship between TOC and Δd is linear and can therefore be described as(7)where a and b are the slope and intercept of the linear relationship, respectively. They are obtained by correlating the values of Δd with the core TOC data. In practice, a must be positive because of the positive correlation of the separation and TOC. Variable b should be equal to or greater than 0% and less than 0.5%, because the TOC contents in the nonsource rocks vary over that range (Wang and Guo, 2000).

Workflow

The new overlay method for estimating TOC content from well logs includes the following steps.

Collect a data set consisting of core TOC data and the corresponding well logs, including GR, DEN, and CNL logs. Confirm that the logs are of good quality.

Present the clay indicator and GR curves in the same track. Next, adjust the scales of the two curves to make sure that the two curves overlie in the nonkerogen segments and are not out of the track boundaries.

CASE STUDIES

To verify that the newly developed method is effective and reliable, the method was applied to shale gas reservoirs in the Sichuan Basin and a well from Passey et al. (1990).

Shale Gas Reservoirs in the Sichuan Basin, China

To apply the new method to our shale gas reservoirs, the effectiveness of the clay indicator in source rock intervals with high-maturity kerogen should first be verified. Figure 5 shows a crossplot of the clay content with the clay indicator. The clay content is in volume percentage, which is derived from Table 1. The strong correlation between the core clay content and clay indicator of gas shale with high-maturity kerogen is reflected by the coefficient of determination (R2) of 0.852. This indicates that the proposed clay indicator is viable and effective for reflecting the clay content in source rocks with high-maturity kerogen.

The crossplot of the clay content from x-ray diffraction with the clay indicator. R2 = coefficient of determination.

Based on the TOC from the sample analysis (Table 2) and log data, the coefficients a and b in equation 7 were obtained following the above workflow. As shown in Figure 6, the formula to estimate the TOC content from the well logs in the YDN shale gas reservoirs can be expressed as(8)Figure 7 shows the estimated results for well SS. The GR and caliper logs are displayed in track 1. The measured depth is displayed in track 2. The porosity logs, including the DEN, CNL, and AC logs, are presented in track 3. Track 4 shows the lateral RT log. Track 5 presents the result of the ΔlogR method, and track 6 displays the new method proposed in this paper. Comparing track 5 with track 6, the curves of both methods overlie in the upper section, which is the nonsource, clay-rich interval of the well. In the middle section, the two curves of both two methods separate, which indicates gas shale. However, the RT has a large fluctuation, which causes the ΔlogR method to fail to estimate the TOC. In the lower section, which is in a carbonate formation, the ΔlogR curves are still separated, whereas the two curves of our new method overlap well. From track 6, it can be readily recognized whether the intervals contain kerogen. Obviously, the interval 727–802 m (2385–2631 ft) is the target reservoir. The last track is a comparison of the TOC from core analysis and well logs. The results derived from the two approaches agree well.

Figure 8 shows the crossplot of the TOC from the core analysis with the separation Δd of well TT. The coefficients a and b in equation 7 were obtained from this figure. The equation for TOC for this well is expressed as(9)The TOC content of well TT was estimated using equation 9, and the results are shown in Figure 9. The logs or curves in Figure 9 are the same as those in Figure 7. The target formation is from 1495 to 1544 m (4905 to 5066 ft), according to Figure 2B. The RT logs of this well also change rapidly within the interval of interest. The curves of the new method overlap in the rock from 1544 to 1546 m (5066 to 5072 ft), which is carbonate rock. However, the ΔlogR method curves are separated. The TOC values calculated from the logs and derived from the core analysis are in good agreement, as shown in the last track.

Table 6 summarizes the prediction statistics from the two fields by comparing the calculated results with the core data. The criteria include R2, root mean square error (RMSE), and average error. As can be observed from the table, the R2 are higher, and the errors are lower. Figure 10 demonstrates the errors between the predicted TOC and the TOC from the core analysis of the two fields. It can be observed that the predicted TOC values are close to the core TOC value, and the absolute errors are mostly lower than 0.4. Using Table 6 and Figure 10, the reliability and effectiveness of the newly proposed method in shale gas reservoirs in the Sichuan Basin were verified.

Table 7 presents a data set from well A taken from Passey et al. (1990). The level of organic metamorphism of the organic matter in the well ranges from 6 to 7; the corresponding Ro ranges are 0.42%–0.48%. The kerogen is type II.

Because it is impossible to acquire log data for all of the intervals in this well, the scales of the GR and clay indicator could not be accurately adjusted. According to the data shown in Table 7, GR_left and GR_right were roughly set to 0.0 and 200.0, respectively, and Icl_left and Icl_right were roughly taken as −0.15 and 0.85, respectively. Nevertheless, the TOC and separation Δd were strongly correlated, as illustrated in Figure 11. The R2 was 0.729, which is close to the R2 (0.750) between the ΔlogR and TOC listed in Passey et al. (1990). The R2 between the TOC and separation Δd may have been higher if the two curves had been accurately scaled. This proves that the new overlay method can be used for source rocks with low-maturity kerogen, which also indirectly confirms the effectiveness of the clay indicator.

In summary, the application of the newly proposed method to three fields in two basins confirms that the method can effectively and reliably estimate TOC content.

DISCUSSION

Among the methods for estimating TOC from well logs, to a certain extent, the overlay methods are better than direct core calibration methods. The ΔlogR model, which overlays AC and RT logs, can distinguish source rocks and nonsource, clay-rich rocks. The Jacobi et al. (2008) method, which overlays grain density and inorganic grain density, can differentiate source rocks and nonsource rocks reasonably well. The method is advanced and accurate, but it cannot be widely used because NMR and geochemical logs are not always available. The ΔlogR method is the most widely used, but it was unable to evaluate the TOC in our study gas shale reservoirs because of the abnormally low RT response. The new method developed in this paper, which overlays GR and the clay indicator, avoids the failures of the ΔlogR method caused by abnormal RT. Another advantage of the new method is that the GR and clay indicator overlie in nonsource reservoirs with oil or water. The relationship between the separation of the curves and maturity cannot be established (similar to ΔlogR) because the GR log is not influenced by maturity, but the new method can be used as an alternative method to estimate TOC content.

The new method is applicable for source rocks with excess radioactivity and that contain little or no K-feldspar. Generally, the K-feldspar content of source rocks is not high (Table 8), which indicates that the method may be widely applicable.

CONCLUSIONS

Based on previous analyses by geoscientists and log analysts of log responses of source rocks and in combination with the GR responses of common minerals in sedimentary rocks, it is believed that the GR responses of source rocks with excess radioactivity and that contain little or no K-feldspar are predominantly affected by clay minerals and organic matter.

Through the analysis of DEN and CNL log responses for common minerals in sedimentary rocks, a practical clay indicator has been established to reflect clay content using DEN and CNL logs. The clay indicator is effective in nonsource rocks. In addition, its effectiveness in source rocks was also directly and indirectly verified.

A new log overlay method was developed to estimate the TOC content of source rocks; the method is applicable to source rocks with excess radioactivity and that contain little or no K-feldspar. The new method was established by overlaying the properly scaled clay indicator curve on the GR log. The separation between the two curves was defined and expressed to estimate TOC consecutively after careful calibration with core data. The new method avoids the failures in estimating TOC caused by abnormal RT. Another advantage is that it can accurately distinguish source and nonsource rocks. It can be used as an alternative method to estimate TOC from well logs.

The new method was successfully applied to two shale gas plays with high-maturation kerogen in the Sichuan Basin, China. The R2 values between the predicted TOC and the experimental results were 0.839 and 0.917, respectively. The RMSE values between the predicted TOC and the experimental results were 0.451 and 0.504, respectively. The absolute errors between the predicted TOC and the experimental results for the two fields were mostly lower than 0.4. In addition, a source rock with low-maturation kerogen from the published literature was used to verify the effectiveness, reliability, and widespread adaptability of the new method.

ACKNOWLEDGMENTS

This study was financially supported by the Major National Oil and Gas Specific Project of China (no. 2011ZX05044 and no. 2016ZX05050), the National Natural Science Foundation of China (no. 41404084), and Chongqing Land Bureau Science and Technology Planning Project (no. CQGT-KJ-2014017). The authors also would like to thank the reviewers for their valuable advice and comments that improved this paper.

Footnotes

EDITOR'S NOTE Color versions of Figures 4–11 can be seen in the online version of this paper.

Manuscript received May 26, 2015.

Revised manuscript received December 30, 2015.

Final acceptance November 18, 2015.

Final acceptance February 22, 2016.

Peiqiang Zhao is a Ph.D. student under Professor Zhiqiang Mao at the China University of Petroleum, Beijing. His interests include petrophysics and the evaluation of formations of unconventional resources. He earned his master’s degree in earth prospecting and information technology from the China University of Petroleum, Beijing, in 2013.

Zhiqiang Mao works as a professor and Ph.D. supervisor at the China University of Petroleum, Beijing. His interests include petrophysics and evaluation of formations using well logs. He earned his Ph.D. in geology and exploration for coal, oil, and natural gas from the postgraduate school of the Research Institute of Petroleum Exploration and Development of Beijing in 1995.

Zhenhua Huang works as an engineer and log analyst at the Chongqing Institute of Geology and Mineral Resources, Chongqing, China. His interests include the exploration and development of technologies for shale gas. He received his master’s degree in earth prospecting and information technology from the China University of Petroleum, Beijing, in 2012.

Chong Zhang works as an associate professor and master tutor at Yangtze University, Wuhan, China. He specializes in rock physics and evaluation of formations using well logs. He earned his Ph.D. in geological resources and geological engineering from the China University of Petroleum, Beijing, in 2010.